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      The myogenic electric organ of Sternopygus macrurus: a non-contractile tissue with a skeletal muscle transcriptome

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          Abstract

          In most electric fish species, the electric organ (EO) derives from striated muscle cells that suppress many muscle properties. In the gymnotiform Sternopygus macrurus, mature electrocytes, the current-producing cells of the EO, do not contain sarcomeres, yet they continue to make some cytoskeletal and sarcomeric proteins and the muscle transcription factors (MTFs) that induce their expression. In order to more comprehensively examine the transcriptional regulation of genes associated with the formation and maintenance of the contractile sarcomere complex, results from expression analysis using qRT-PCR were informed by deep RNA sequencing of transcriptomes and miRNA compositions of muscle and EO tissues from adult S. macrurus. Our data show that: (1) components associated with the homeostasis of the sarcomere and sarcomere-sarcolemma linkage were transcribed in EO at levels similar to those in muscle; (2) MTF families associated with activation of the skeletal muscle program were not differentially expressed between these tissues; and (3) a set of microRNAs that are implicated in regulation of the muscle phenotype are enriched in EO. These data support the development of a unique and highly specialized non-contractile electrogenic cell that emerges from a striated phenotype and further differentiates with little modification in its transcript composition. This comprehensive analysis of parallel mRNA and miRNA profiles is not only a foundation for functional studies aimed at identifying mechanisms underlying the transcription-independent myogenic program in S. macrurus EO, but also has important implications to many vertebrate cell types that independently activate or suppress specific features of the skeletal muscle program.

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          Most cited references54

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          Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR

          Background Control genes, which are often referred to as housekeeping genes, are frequently used to normalise mRNA levels between different samples. However, the expression level of these genes may vary among tissues or cells and may change under certain circumstances. Thus, the selection of housekeeping genes is critical for gene expression studies. To address this issue, 7 candidate housekeeping genes including several commonly used ones were investigated in isolated human reticulocytes. For this, a simple ΔCt approach was employed by comparing relative expression of 'pairs of genes' within each sample. On this basis, stability of the candidate housekeeping genes was ranked according to repeatability of the gene expression differences among 31 samples. Results Initial screening of the expression pattern demonstrated that 1 of the 7 genes was expressed at very low levels in reticulocytes and was excluded from further analysis. The range of expression stability of the other 6 genes was (from most stable to least stable): GAPDH (glyceraldehyde 3-phosphate dehydrogenase), SDHA (succinate dehydrogenase), HPRT1 (hypoxanthine phosphoribosyl transferase 1), HBS1L (HBS1-like protein) and AHSP (alpha haemoglobin stabilising protein), followed by B2M (beta-2-microglobulin). Conclusion Using this simple approach, GAPDH was found to be the most suitable housekeeping gene for expression studies in reticulocytes while the commonly used B2M should be avoided.
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            R: A Language and environmental for statistical computing

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              Comprehensive algorithm for quantitative real-time polymerase chain reaction.

              Quantitative real-time polymerase chain reactions (qRT-PCR) have become the method of choice for rapid, sensitive, quantitative comparison of RNA transcript abundance. Useful data from this method depend on fitting data to theoretical curves that allow computation of mRNA levels. Calculating accurate mRNA levels requires important parameters such as reaction efficiency and the fractional cycle number at threshold (CT) to be used; however, many algorithms currently in use estimate these important parameters. Here we describe an objective method for quantifying qRT-PCR results using calculations based on the kinetics of individual PCR reactions without the need of the standard curve, independent of any assumptions or subjective judgments which allow direct calculation of efficiency and CT. We use a four-parameter logistic model to fit the raw fluorescence data as a function of PCR cycles to identify the exponential phase of the reaction. Next, we use a three-parameter simple exponent model to fit the exponential phase using an iterative nonlinear regression algorithm. Within the exponential portion of the curve, our technique automatically identifies candidate regression values using the P-value of regression and then uses a weighted average to compute a final efficiency for quantification. For CT determination, we chose the first positive second derivative maximum from the logistic model. This algorithm provides an objective and noise-resistant method for quantification of qRT-PCR results that is independent of the specific equipment used to perform PCR reactions.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                14 April 2016
                2016
                : 4
                : e1828
                Affiliations
                [1 ]Department of Biology, New Mexico State University , Las Cruces, NM, United States
                [2 ]Systemix Institute , Redmond, WA, United States
                Article
                1828
                10.7717/peerj.1828
                4841239
                27114860
                9ec7b60d-2ff6-464e-9097-ef4e1a7c97e0
                ©2016 Pinch et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 8 August 2015
                : 29 February 2016
                Funding
                Funded by: NIH SCORE
                Award ID: 1SC1GM092297-01A1
                Funded by: NSF
                Award ID: CNS-1248109
                Funded by: Howard Hughes Medical Institute Science Education
                Award ID: 5200693
                The following grants funded this work: NIH SCORE Grant 1SC1GM092297-01A1 (GAU), NSF Grant CNS-1248109 (GAU), and Howard Hughes Medical Institute Science Education Grant 5200693. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Aquaculture, Fisheries and Fish Science
                Developmental Biology
                Evolutionary Studies
                Molecular Biology

                transcriptome,skeletal muscle,electric organ,electric fish,myogenic regulatory factors,myogenic transcriptome,myogenic phenotype,electrocyte phenotype

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